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Abstract:

A method, a system and a computer-readable medium for reconstructing a
vehicle moving path are provided. In the method, a plurality of vehicle
recognition results of a plurality of first monitoring frames captured by
a plurality of first type road monitors are received and compared to find
at least one similar vehicle. Next, according to a disposition location
of each first road monitor and the comparison result of each vehicle, at
least one passing spot and a driving time that each vehicle moves between
the disposition locations are estimated. Then, tracking data of at least
one moving object appeared in multiple second monitoring frames captured
by multiple second type road monitors disposed in the passing spots is
inquired. Finally, the vehicles are compared with the tracked moving
objects to find the moving object associated with each vehicle, so as to
construct a complete moving path of each vehicle.

Claims:

1. A method for reconstructing a vehicle moving path, comprising:
receiving vehicle recognition data, wherein the vehicle recognition data
comprises a vehicle recognition result of each of a plurality of first
monitoring frames captured by a plurality of first type road monitors;
comparing the vehicle recognition result of each of the first monitoring
frames to find at least one similar vehicle; according to a disposition
location of each of the first type road monitors and a comparison result
of the at least one vehicle, estimating at least one passing spot and a
driving time that the at least one vehicle moves between the disposition
locations; inquiring moving object tracking data, wherein the moving
object tracking data comprises tracking data of at least one moving
object appeared in a plurality of second monitoring frames captured by a
plurality of second type road monitors disposed in the at least one
passing spot; and comparing the at least one vehicle with the at least
one moving object to find the moving object associated with each of the
at least one vehicle, so as to construct a complete moving path of the at
least one vehicle.

2. The method for reconstructing the vehicle moving path as claimed in
claim 1, wherein the step of comparing the vehicle recognition result of
each of the first monitoring frames to find the at least one similar
vehicle comprises: comparing at least one vehicle feature of the vehicles
appeared in the first monitoring frames to recognize the at least one
similar vehicle.

3. The method for reconstructing the vehicle moving path as claimed in
claim 2, wherein the step of comparing the at least one vehicle feature
of the vehicles appeared in the first monitoring frames to recognize the
at least one similar vehicle comprises: capturing a first plate number
and a second plate number of any two vehicles appeared in the first
monitoring frames; calculating a minimum number of edit operations
required for transforming the first plate number to the second plate
number, and comparing the minimum number of the edit operations with a
threshold value; and determining the two vehicles to be the similar
vehicle when the minimum number of the edit operations is smaller than or
equal to the threshold value.

4. The method for reconstructing the vehicle moving path as claimed in
claim 2, wherein the at least one vehicle feature comprises a license
plate, a vehicle color or a vehicle type.

5. The method for reconstructing the vehicle moving path as claimed in
claim 1, wherein the step of estimating the at least one passing spot and
the driving time that the at least one vehicle moves between the
disposition locations according to the disposition location of each of
the first type road monitors and the comparison result of the at least
one vehicle comprises: finding the first monitoring frames where the at
least one vehicle appears and the corresponding disposition locations
according to the comparison result of the at least one vehicle; and
inquiring historical driving data to determine the at least one passing
spot and the driving time that the at least one vehicle moves between the
disposition locations, and outputting a driving data collection, wherein
the historical driving data comprises the at least one passing spot and
the corresponding driving time that the vehicles used to move between the
disposition locations.

6. The method for reconstructing the vehicle moving path as claimed in
claim 1, wherein before the step of inquiring the moving object tracking
data, the method further comprises: storing a position, a time, a size, a
color and a keyframe of the at least one moving object appeared in the
second monitoring frames into a moving object tracking database.

7. The method for reconstructing the vehicle moving path as claimed in
claim 6, wherein the step of comparing the at least one vehicle with the
at least one moving object to find the moving object associated with the
at least one vehicle, so as to construct the complete moving path of the
at least one vehicle comprises: receiving the driving data collection
corresponding to each of the at least one vehicle; sorting the driving
data collections according to the driving time of each of the driving
data collections; finding all of the second type road monitors probably
passed by according to a geographic position association of the at least
one passing spot in the driving data collections; inquiring the moving
object tracking database to obtain the at least one moving object
tracking data of the moving object associated with the at least one
vehicle according to geographic position data of each of the found second
type road monitors; and constructing the complete moving path of each of
the at least one vehicle according to the driving data collection of the
at least one vehicle and the at least one moving object tracking data of
the moving object associated with the at least one vehicle.

8. The method for reconstructing the vehicle moving path as claimed in
claim 7, wherein the step of obtaining the moving object associated with
the at least one vehicle comprises: comparing time information of the at
least one vehicle and the at least one moving object to search the moving
object with an appearing time closest to a historical statistic time
interval, so as to establish association with the at least one vehicle.

9. The method for reconstructing the vehicle moving path as claimed in
claim 7, wherein the step of obtaining the moving object associated with
each of the at least one vehicle comprises: comparing space information
of the at least one vehicle and the at least one moving object to search
the moving object appeared at two adjacent intersections or within a
specific distance, so as to establish association with each of the at
least one vehicle.

10. The method for reconstructing the vehicle moving path as claimed in
claim 7, wherein the step of obtaining the moving object associated with
the at least one vehicle comprises: representing each of the at least one
vehicle and each of the at least one moving object by a corresponding
feature vector matrix; obtaining a similarity between each two of the
feature vector matrices; and establishing association between the vehicle
and the moving object corresponding to the feature vector matrix having
the highest similarity.

11. The method for reconstructing the vehicle moving path as claimed in
claim 7, wherein after the step of obtaining the at least one moving
object tracking data of the moving object associated with the at least
one vehicle, the method further comprises: deducing a normal moving path
according to the at least one passing spot passed by the at least one
vehicle and the driving time; and calculating a difference between the at
least one moving object tracking data and the normal moving path to
filter out unreasonable moving object tracking data.

12. The method for reconstructing the vehicle moving path as claimed in
claim 11, wherein after the step of calculating the difference between
the at least one moving object tracking data and the normal moving path
to filter out the unreasonable moving object tracking data, the method
further comprises: deducing a possible moving range of the at least one
vehicle according to a vehicle speed and a moving direction in a motion
model, so as to find a highest possible moving object tracking data from
the moving object tracking data already filtering out the unreasonable
moving object tracking data.

13. The method for reconstructing the vehicle moving path as claimed in
claim 7, wherein after the step of constructing the complete moving path
of the at least one vehicle, the method further comprises: establishing
an association between the complete moving path of the at least one
vehicle and at least one keyframe to serve as a basis for searching the
at least one vehicle according to the vehicle recognition result of each
of the first monitoring frames and the at least one keyframe included in
the at least one moving object tracking data.

14. The method for reconstructing the vehicle moving path as claimed in
claim 1, wherein the first type road monitor supports license plate
recognition, and the second type road monitor does not support the
license plate recognition.

15. A system for reconstructing a vehicle moving path, comprising: a
vehicle searching module, configured to receive a vehicle recognition
result of each of a plurality of first monitoring frames captured by a
plurality of first type road monitors, and compare the vehicle
recognition results of the first monitoring frames to find at least one
similar vehicle, and according to a disposition location of each of the
first type road monitors and a comparison result of the at least one
vehicle, estimate at least one passing spot and a driving time that the
at least one vehicle moves between the disposition locations; and a path
reconstructing module, configured to inquire tracking data of at least
one moving object appeared in a plurality of second monitoring frames
captured by a plurality of second type road monitors disposed at the at
least one passing spot, and compare the at least one vehicle with the at
least one moving object to find the moving object associated with each of
the at least one vehicle, so as to construct a complete moving path of
the at least one vehicle.

16. The system for reconstructing the vehicle moving path as claimed in
claim 15, wherein the vehicle searching module comprises: a similar
vehicle comparison unit, configured to compare at least one vehicle
feature of the vehicles appeared in the first monitoring frames to
recognize the at least one similar vehicle. a driving information
providing unit, configured to provide historical driving data comprising
the at least one passing spot and the corresponding driving time that the
vehicles used to move between the disposition locations; and a passing
spot estimation unit, configured to find the first monitoring frames
where the at least one vehicle appears and the corresponding disposition
locations according to the comparison result of the at least one vehicle,
and inquire the historical driving data to determine the at least one
passing spot and the driving time that the at least one vehicle moves
between the disposition locations, and outputting a driving data
collection.

17. The system for reconstructing the vehicle moving path as claimed in
claim 16, wherein the similar vehicle comparison unit captures a first
plate number and a second plate number of any two vehicles appeared in
the first monitoring frames, calculates a minimum number of edit
operations required for transforming the first plate number to the second
plate number, and compares the minimum number of the edit operations with
a threshold value, and determines the two vehicles to be the similar
vehicle when the minimum number of the edit operations is smaller than or
equal to the threshold value.

18. The system for reconstructing the vehicle moving path as claimed in
claim 16, wherein the at least one vehicle feature comprises a license
plate, a vehicle color or a vehicle type.

19. The system for reconstructing the vehicle moving path as claimed in
claim 15, wherein the path reconstructing module comprises: a moving
object tracking database, configured to store a position, a time, a size,
a color and a keyframe of the at least one moving object appeared in the
second monitoring frames; a tracking data inquiry unit, configured to
receive the driving data collection corresponding to each of the at least
one vehicle, sorting the driving data collections according to the
driving time of each of the driving data collections, find all of the
second type road monitors probably passed by according to a geographic
position association of the at least one passing spot in the driving data
collections, and inquire the moving object tracking database to obtain
the at least one moving object tracking data of the moving object
associated with the at least one vehicle according to geographic position
data of each of the found second type road monitors.

20. The system for reconstructing the vehicle moving path as claimed in
claim 19, wherein the tracking data inquiry unit compares time
information of the at least one vehicle and the at least one moving
object to search the moving object with an appearing time closest to a
historical statistic time interval, so as to establish association with
each of the at least one vehicle.

21. The system for reconstructing the vehicle moving path as claimed in
claim 19, wherein the tracking data inquiry unit compares space
information of the at least one vehicle and the at least one moving
object to search the moving object appeared at two adjacent intersections
or within a specific distance, so as to establish association with each
of the at least one vehicle.

22. The system for reconstructing the vehicle moving path as claimed in
claim 19, wherein the tracking data inquiry unit represents each of the
at least one vehicle and each of the at least one moving object by a
corresponding feature vector matrix, obtains a similarity between each
two of the feature vector matrices, and establishes association between
the vehicle and the moving object corresponding to the feature vector
matrix having the highest similarity.

23. The system for reconstructing the vehicle moving path as claimed in
claim 19, wherein the path reconstructing module further comprises: a
linear regression filter unit, configured to deduce a normal moving path
according to the at least one passing spot passed by the at least one
vehicle and the driving time, and calculate a difference between the at
least one moving object tracking data and the normal moving path to
filter out unreasonable moving object tracking data.

24. The system for reconstructing the vehicle moving path as claimed in
claim 19, wherein the path reconstructing module further comprises: a
motion model filter unit, configured to deduce a possible moving range of
the at least one vehicle according to a vehicle speed and a moving
direction in a motion model, so as to find a highest possible moving
object tracking data from the moving object tracking data already
processed by linear regression filtering.

25. The system for reconstructing the vehicle moving path as claimed in
claim 19, further comprising: a keyframe association module, comprising:
a keyframe database, configured to store at least one keyframe generated
according to the vehicle recognition result of each of the first
monitoring frames and the at least one moving object tracking data; and
an association establishing unit, configured to establish an association
between the complete moving path of the at least one vehicle and at least
one keyframe to serve as a basis for searching the at least one vehicle.

26. The system for reconstructing the vehicle moving path as claimed in
claim 15, wherein the first type road monitor supports license plate
recognition, and the second type road monitor does not support the
license plate recognition.

27. A computer-readable medium, which records a computer program to be
loaded into an electronic device to execute the method for reconstructing
the vehicle moving path as claimed in claim 1.

Description:

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the priority benefit of Taiwan application
serial no. 99146378, filed Dec. 28, 2010. The entirety of the
above-mentioned patent application is hereby incorporated by reference
herein and made a part of this specification.

BACKGROUND

[0002] 1. Field of the Disclosure

[0003] The disclosure relates to a method, a system and a
computer-readable medium for vehicle tracking and reconstructing a
vehicle moving path.

[0004] 2. Description of Related Art

[0005] Conventionally, a position of a moving vehicle can be obtained
through a global positioning system (GPS). An operation principle of such
method is to install a GPS signal receiver on a target vehicle for
receiving GPS signals in real time and upload positioning information to
a post end host through a wireless communication interface, so as to
track the position of the target vehicle. Such method is generally
applied for fleet management. However, such method is limited in
applications, especially in urban areas when the GPS signals are shielded
by buildings and the receiver cannot receive the GPS signals. Moreover,
since an additional device has to be installed on the target vehicle, it
is not applicable for obtaining positions of non-specific targets. In
addition, a method for tracking vehicles through monitoring images
obtained by cameras disposed at street intersections has been provided.

[0006] A greatest challenge of tracking a specific target through
different cameras is that moving objects detected by different cameras
have to be re-identified to remove repeat data and maintain consistency
of target information. Conventionally, cameras with an overlapped
monitoring range are used, and based on a physical characteristic that
the moving objects detected by the cameras in the overlapped region at a
same time and a same position should be a same target object, the moving
object detecting information of a plurality of the cameras are
integrated. Such method depends on correctness of a moving object
detecting algorithm and accuracy of coordinate conversion. Generally,
when the monitoring images captured by the road cameras are analysed, an
object positioning error caused by distortions of the moving object
detecting algorithm and the coordinate conversion can be more than a half
of a size of the target object, particularly, the greater a monitoring
range is, the larger the error is, and the error is probably greater than
the size of the target object. Therefore, when a plurality of moving
objects are simultaneously moving within a same range, a chance of
re-identification error is very high. In order to mitigate the above
problem, a general method is to ameliorate the moving object detecting
algorithm to improve information correctness of the object detection, or
ameliorate the coordinate conversion to reduce positioning distortion.

[0007] In an actual application, since resolutions of the cameras disposed
at the street intersections are not high, and monitoring ranges are
relatively wide, quality of the obtained images is poor, so that it is
hard to obtain a better result through the moving object detecting
algorithm. Therefore, improvement effectiveness of ameliorating the
moving object detecting algorithm or ameliorating the coordinate
conversion is limited. Moreover, the moving object detecting algorithm is
greatly influenced by a weather factor, and once it is used in outdoor
applications, the generated errors are hard to be accepted. Due to the
influences of the above problems, when the moving object is tracked
through different cameras, correctness of a generated moving path is not
high.

SUMMARY OF THE DISCLOSURE

[0008] The disclosure is directed to a method, a system and a
computer-readable medium for reconstructing a vehicle moving path, by
which a vehicle recognition system and road monitors are simultaneously
used to reconstruct the vehicle moving path.

[0009] The disclosure provides a method for reconstructing a vehicle
moving path. In the method, vehicle recognition data is received, which
includes a vehicle recognition result of each of a plurality of first
monitoring frames captured by a plurality of first type road monitors.
Then, the vehicle recognition results of the first monitoring frames are
compared to find at least one similar vehicle. Next, according to a
disposition location of each of the first type road monitors and a
comparison result of the at least one vehicle, at least one passing spot
and a driving time that the at least one vehicle moves between the
disposition locations are estimated. Then, moving object tracking data is
inquired, which includes tracking data of at least one moving object
appeared in a plurality of second monitoring frames captured by a
plurality of second type road monitors disposed in the at least one
passing spot. Finally, the at least one vehicle is compared with the at
least one moving object to find the moving object associated with each of
the at least one vehicle, so as to construct a complete moving path of
each of the at least one vehicle.

[0010] The disclosure provides a system for reconstructing a vehicle
moving path, which includes a vehicle searching module and a path
reconstructing module. The vehicle searching module receives a vehicle
recognition result of each of a plurality of first monitoring frames
captured by a plurality of first type road monitors, and compares the
vehicle recognition results of the first monitoring frames to find at
least one similar vehicle, and according to a disposition location of
each of the first type road monitors and a comparison result of the at
least one vehicle, the vehicle searching module estimates at least one
passing spot and a driving time that the at least one vehicle moves
between the disposition locations. The path reconstructing module
inquires tracking data of at least one moving object appeared in a
plurality of second monitoring frames captured by a plurality of second
type road monitors disposed in the at least one passing spot, and
compares the at least one vehicle with the at least one moving object to
find the moving object associated with each of the at least one vehicle,
so as to construct a complete moving path of the at least one vehicle.

[0011] The disclosure provides a computer-readable medium, which records a
computer program to be loaded into an electronic device to execute
following steps. First, vehicle recognition data is received, which
includes a vehicle recognition result of each of a plurality of first
monitoring frames captured by a plurality of first type road monitors.
Then, the vehicle recognition results of the first monitoring frames are
compared, so as to find at least one similar vehicle. Then, according to
a disposition location of each of the first type road monitors and the
comparison result of each vehicle, at least one passing spot and a
driving time that each vehicle moves between the disposition locations
are estimated. Then, tracking data of one moving object is inquired,
which includes tracking data of at least one moving object appeared in a
plurality of second monitoring frames captured by a plurality of second
type road monitors disposed in the passing spots. Finally, the vehicles
are compared with the moving objects to find the moving object associated
with each of the vehicles, so as to construct a complete moving path of
each of the vehicles.

[0012] According to the above descriptions, in the method, the system and
the computer-readable medium for reconstructing a vehicle moving path of
the disclosure, a vehicle recognition technique and a moving object
tracking technique are used in collaboration with a vehicle comparison
technique and a passing spot and time estimation technique to improve
correctness of reconstructing the complete vehicle moving path, and a
keyframe association establishing technique is used to improve
correctness for inquiring related information of the target vehicle.

[0013] In order to make the aforementioned and other features and
advantages of the disclosure comprehensible, several exemplary
embodiments accompanied with figures are described in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are included to provide a further
understanding of the disclosure, and are incorporated in and constitute a
part of this specification. The drawings illustrate embodiments of the
disclosure and, together with the description, serve to explain the
principles of the disclosure.

[0015] FIG. 1 is a block diagram illustrating a system for reconstructing
a vehicle moving path according to a first exemplary embodiment of the
disclosure.

[0016]FIG. 2 is a flowchart illustrating a method for reconstructing a
vehicle moving path according to the first exemplary embodiment of the
disclosure.

[0017] FIG. 3 is a schematic diagram illustrating a system for
reconstructing a vehicle moving path according to a second exemplary
embodiment of the disclosure.

[0018]FIG. 4 is a flowchart illustrating a method for reconstructing a
vehicle moving path according to the second exemplary embodiment of the
disclosure.

[0019]FIG. 5(a) and FIG. 5(b) are examples of calculating a minimum
number of edit operations according to an exemplary embodiment of the
disclosure.

[0020]FIG. 6 is a schematic diagram of a linear regression filtering
result according to an exemplary embodiment of the disclosure.

[0021] FIG. 7 is a schematic diagram of a motion model according to an
exemplary embodiment of the disclosure.

DETAILED DESCRIPTION OF DISCLOSED EMBODIMENTS

[0022] Since cost of road monitors having a vehicle recognition function
is relatively high, they are generally disposed at several major
intersections, and general road monitors are disposed at other
intersections. However, variation of types, speeds and directions of
moving vehicles on the road is tremendous, and if vehicle recognition
results of only several road monitors are used to reconstruct moving
paths of the vehicles, correctness thereof cannot be guaranteed,
especially when the vehicle passes a plurality of intersections,
correctness of the moving path thereof is greatly reduced. In order to
compensate information of the intersections without the vehicle
recognition system, according to the method of the disclosure, the
vehicle recognition system and the road monitors with relatively low cost
compared to that having the vehicle recognition function are
simultaneously used, and moving object tracking data generated according
to a moving object tracking technique is used to compensate inadequacy of
the vehicle paths generated only according to the vehicle recognition
results.

[0023] FIG. 1 is a block diagram illustrating a system for reconstructing
a vehicle moving path according to a first exemplary embodiment of the
disclosure. FIG. 2 is a flowchart illustrating a method for
reconstructing a vehicle moving path according to the first exemplary
embodiment of the disclosure. Referring to FIG. 1 and FIG. 2, the system
100 for reconstructing a vehicle moving path includes a vehicle searching
module 110 and a path reconstructing module 120. The method of the
present exemplary embodiment is described in detail below with reference
of FIG. 2.

[0024] First, the vehicle searching module 110 receives vehicle
recognition data from a vehicle recognition system (not shown) (step
S210), where the vehicle recognition data includes a vehicle recognition
result of each of a plurality of first monitoring frames captured by a
plurality of first type road monitors. The first type road monitors
support a license plate recognition function, and the first monitoring
frames captured by the first type road monitors are sent to the vehicle
recognition system to recognize the vehicles. The vehicle searching
module 110 of the present exemplary embodiment receives the vehicle
recognition results output by the vehicle recognition system.

[0025] Then, the vehicle searching module 110 compares the vehicle
recognition result of each of the first monitoring frames to find at
least one similar vehicle (step S220), and according to a disposition
location of each of the first type road monitors and the comparison
result of each vehicle, the vehicle searching module 110 estimates at
least one passing spot and a driving time that each vehicle moves between
the disposition locations (S230). In detail, since cost of the first type
road monitors is relatively high, they are generally disposed at major
intersections, even if a similar vehicle is appeared at two of the major
intersections, a vehicle moving path between the two intersections cannot
be determined. However, in the present exemplary embodiment, the possible
passing spots and driving time that each vehicle moves between the two
intersections are still obtained based on historical statistical
information, so as to serve as basis for post vehicle tracking.

[0026] Then, the path reconstructing module 120 inquires moving object
tracking data, which includes tracking data of at least one moving object
appeared in a plurality of second monitoring frames captured by a
plurality of second type road monitors disposed in the passing spots
(step S240). The second type road monitors do not support the licence
plate recognition function, though the monitoring frames captured by the
second type road monitors can still be used to track the moving objects
(i.e. the vehicles) appeared in the monitoring frames according to a
moving object tracking technique for serving as a basis for
reconstructing the moving path.

[0027] Finally, the path reconstructing module 120 compares the at least
one vehicle found by the vehicle searching module 110 and the at least
one inquired moving object according to time, space information and
feature information such as color histograms of the vehicle and the
moving object, so as to find the moving object associated with each of
the vehicles, and accordingly construct a complete moving path of each of
the vehicles (step S250). In brief, the path reconstructing module 120
finds the possible moving object appeared in the second type road
monitors according to a time point that the vehicle found by the vehicle
searching module 110 appear in each of the first type road monitors, and
constructs the complete moving path the vehicle according to the vehicle
recognition result and the moving object tracking result.

[0028] In overall, according to the method for reconstructing the vehicle
moving path of the present exemplary embodiment, the output results of
the vehicle recognition system and the moving object tracking system are
integrated to construct the complete moving path of each of the vehicles,
so as to improve correctness of information and reconstruct complete
vehicle moving paths.

[0029] It should be noticed that after the complete moving path of each of
the vehicles is reconstructed, shooting time of keyframes are obtained to
further find the keyframes corresponding to the vehicle moving path, and
an association between the vehicle moving path and the keyframes is
established to serve as basis for post vehicle moving path inquiry.
Another exemplary embodiment is provided below for detailed descriptions.

[0030] FIG. 3 is a schematic diagram illustrating a system for
reconstructing a vehicle moving path according to a second exemplary
embodiment of the disclosure. FIG. 4 is a flowchart illustrating a method
for reconstructing a vehicle moving path according to the second
exemplary embodiment of the disclosure. Referring to FIG. 3 and FIG. 4,
the system 300 for reconstructing a vehicle moving path includes a
vehicle searching module 310, a path reconstructing module 320 and a
keyframe association module 330. The method of the present exemplary
embodiment is described in detail below with reference of FIG. 4.

[0031] First, the vehicle searching module 310 receives vehicle
recognition results from a vehicle recognition system 32, and compares
the vehicle recognition results of the first monitoring frames to find at
least one similar vehicle appeared in the first monitoring frames (step
S410).

[0032] In detail, the vehicle searching module 310 may include a similar
vehicle comparison unit 312, a driving information providing unit 314 and
a passing spot estimation unit 316. The similar vehicle comparison unit
312 is used for comparing a vehicle feature of each of the vehicles
appeared in the first monitoring frames to recognize the similar vehicle
(step S411). The vehicle feature used for recognizing the similar vehicle
may include a vehicle licence plate, a vehicle color, and a vehicle type,
etc., which is not limited by the disclosure.

[0033] Taking the licence plate as an example, in the present exemplary
embodiment, a difference between plate numbers of two vehicles is defined
as an edit distance, and a magnitude of the edit distance is used to
determine whether the two vehicles are the same or similar.

[0034] In detail, the edit distance is defined as a minimum number of edit
operations required for transforming a character string A to a character
string B between two character strings A and B, and a standard edit
operation includes replacing a single character and inserting a
character. For example, FIG. 5(a) and FIG. 5(b) are examples of
calculating the minimum number of edit operations according to an
exemplary embodiment of the disclosure. In a plate image 520 of FIG.
5(a), tail numbers 88 of a plate image 510 are removed, and a minimum
number of edit operations required for achieving such difference is 2.
Moreover, in a plate image 540 of FIG. 5(b), a front code Q of a plate
image 530 is removed, and a minimum number of edit operations required
for achieving such difference is 1. The above edit distance can be used
to quantize the difference between the plate numbers, and a magnitude of
the minimum number of edit operations can be used to determine whether
the two vehicles are the similar vehicle.

[0035] According to the above descriptions, the similar vehicle comparison
unit 312, for example, captures plate numbers (i.e. a first plate number
and a second plate number) of any two vehicles appeared in the first
monitoring frames, and calculates a minimum number of edit operations
required for transforming the first plate number to the second plate
number, and compares it with a threshold value, where when the minimum
number of the edit operations is smaller than or equal to the threshold
value, the two vehicles are determined to be the similar vehicle.

[0036] Referring to FIG. 3, the passing spot estimation unit 316 finds the
first monitoring frames where each of the vehicles appears and the
corresponding disposition locations thereof according to the comparison
result of each vehicle output by the similar vehicle comparison unit 312
(step S412), and inquires historical driving data provided by the driving
information providing unit 314 to determine at least one passing spot and
a driving time that each vehicle moves between the disposition locations,
and finally outputs a driving data collection (step S413).

[0037] In detail, the driving information providing unit 314 is used for
storing and providing the historical driving data including at least one
passing spot and a corresponding driving time that the vehicles used to
move between the disposition locations of the first type road monitors.
Where, the driving information providing unit 314, for example, analyses
historical traffic data of each of the intersections in advance, and, for
example, establishes a driving timetable of each of the intersections and
moving paths between the connected intersections according to a mean and
a standard deviation of statistical analysis to serve as basis for
determining the vehicle passing spots and the driving time.

[0038] Moreover, during a system operation period, the passing spot
estimation unit 316 receives the vehicle comparison results output by the
similar vehicle comparison unit 312, and estimates a chance that a target
vehicle appears at each of the intersections according to the historical
traffic data of each of the intersections, so as to generate a primary
passing intersection data collection. Then, the estimated primary passing
intersection data collection is compared to the driving timetable of each
of the intersections to remove data unreasonable in time (for example,
too long or short driving time interval), so as to generate a secondary
passing intersection data collection.

[0039] Then, the path reconstructing module 320 inquires tracking data of
at least one moving object appeared in a plurality of second monitoring
frames captured by a plurality of second type road monitors disposed in
the passing spots, and compares each of the vehicles and the moving
objects according to time and space information and feature information
such as color histograms, so as to find the moving object associated with
each of the vehicles. Where, regarding the time information, the one
closest to a past statistical target value is used to establish the
association, for example, according to a past statistical result, time
intervals of 99% of the moving objects are between 3 seconds and 5
seconds, and the moving objects closest to the average 4 seconds has a
highest association degree. Regarding the space information, the
association is established by searching the moving objects appeared at
two adjacent intersections or within a certain specific distance. The
time and space information can be integrated into speed information, and
the associations can be established according to the past statistical
results. The feature information is represented by a feature vector
matrix, and similarity of two feature vector matrices is calculated.
Association of the two feature vector matrices can be calculated
according to a general correlation coefficient method to obtain the
similarity, for example, the pearson correlation coefficient or a
geometric distance correlation coefficient, etc. While the above
comparison is performed, the path reconstructing module 320 further
removes unreasonable moving object tracking data according to a linear
regression filter method, and connects the moving object tracking data as
a moving trail according to a time and space motion model, so as to
construct a complete and correct moving path of each of the vehicles
(step S420).

[0040] In detail, the path reconstructing module 320 includes a moving
object tracking database 322, a tracking data inquiry unit 324, a linear
regression filter unit 326 and a motion model filter unit 328. The moving
object tracking database 322 is used for storing analysis data analysed
by a moving object tracking system 34 such as a position, a time, a size,
a color and a keyframe of each of the moving objects appeared in the
second monitoring frames. The moving object tracking system 34 tracks the
moving objects appeared in the second monitoring frames captured by the
second type road monitors, and analyses the position, the time, the size,
the color and the keyframe of each of the moving objects appeared in the
second monitoring frames, and stores the analysis results into the moving
object tracking database 322. The second type road monitors do not
support the licence plate recognition function, though the monitoring
frames captured by the second type road monitors are sent to the moving
object tracking system 34, and the moving object tracking system 34
tracks the moving objects.

[0041] The tracking data inquiry unit 324 receives the driving data
collection corresponding to each of the vehicles that is output by the
passing spot estimation unit 316 of the vehicle searching module 310,
sorts the driving data collections according to the driving time in each
of the driving data collections (step S421), finds all of the second type
road monitors probably passed by according to geographic position
associations of the passing spots in the driving data collections (step
S422), and inquires the moving object tracking database 322 to obtain the
moving object tracking data of the moving object associated with each of
the vehicles according to geographic position data of each of the found
second type road monitors (step S423).

[0042] In detail, the path reconstructing module 320 has two data input
sources, where a first data input source is the data generated according
to the moving object tracking technique, and such data includes
information such as position information, time, size, and keyframe, etc.
of the moving object, and during the operation period of the system, such
data is continuously generated and stored in the data storage medium
(i.e. the moving object tracking database 322) of the system; a second
data input source is the passing intersection data collection output by
the vehicle searching module 310. After the tracking data inquiry unit
324 of the path reconstructing module 320 receives the passing
intersection data collection, the tracking data inquiry unit 324 sorts
the passing intersection data collections according to each intersection
passing time, finds all of the road monitors probably passed by according
to geographic position associations thereof, and obtains the
corresponding moving object tracking data from the moving object tracking
database 322 according to geographic position information of the road
monitors.

[0043] It should be noticed that the path reconstructing module 320
further includes the linear regression filter unit 326 and the motion
model filter unit 328, which are used to filter out unreasonable moving
object tracking data. A method for the path reconstructing module 320
reconstructing the moving path includes two stages, by which the
unreasonable moving object tracking data is first filtered out according
to a linear regression filter method, and then the moving objects
tracking data is connected as a moving trail according to the time and
space motion model.

[0044] The linear regression filter unit 326 deduces a normal moving path
according to the passing spots passed by each of the vehicles and the
driving time, and calculates a difference between the moving object
tracking data and the normal moving path to filter out the unreasonable
moving object tracking data (step S424). In detail, according to the
passing intersection data collection of the target vehicle obtained in
the above step, possible time ranges that the target vehicle passes the
other intersections only installed with the road monitors can be deduced,
and the moving object tracking data is obtained from the moving object
tracking database 322. Moreover, the normal moving path deduced in the
aforementioned step is used as a reference to calculate time and space
differences of all of the moving object tacking data, so as to filter out
the unreasonable tracking data.

[0045] For example, FIG. 6 is a schematic diagram of a linear regression
filtering result according to an exemplary embodiment of the disclosure.
Referring to FIG. 6, the linear regression filtering operation of the
present exemplary embodiment is performed in allusion to each batch of
the original moving object tracking data, by which a distance between the
tracking data and the normal moving path is calculated, and outliers are
filtered to obtain a reasonable moving object tracking data.

[0046] On the other hand, the motion model filter unit 328 deduces a
possible moving range of each of the vehicles according to a vehicle
speed and a moving direction in a motion model, so as to find a highest
possible moving object tracking data from the moving object tracking data
generated by the linear regression filter unit 326 (step S425). In
detail, since in most cases, the number of the moving vehicles in a same
area is plural, and limited by a road condition, moving directions of the
vehicles are probably the same (either in the same direction or opposite
direction), and due to a positioning error in tracking of the moving
object, a plurality of objects are probably located in a same location at
a same time, especially when vehicles in a counter lane pass by the
target vehicle. Therefore, after the linear regression filtering, a
motion model is used to handle the remaining moving object tracking data
to reduce an influence of the above situation. Since the tracked target
is a vehicle, and motion of the vehicle is limited by physical laws, for
example, a speed and a changing rate of the moving direction, etc., in
the present exemplary embodiment, a deduced motion model is used to
select the highest possible moving object tracking data.

[0047] For example, FIG. 7 is a schematic diagram of a motion model
according to an exemplary embodiment of the disclosure. Referring to FIG.
7, a vector is formed by a previous position P1 and a current
position P2 of the vehicle, and a possible moving range is
established at the current location P2, where d represents a maximum
moving distance of the vehicle that is obtained according to the
historical data, and θ is a range angle. Based on such possible
moving range, the outliers (for example, a position Q1) can be
filtered out, and similarity comparison is performed to all of the
inliers (for example, a position Q2) to find the most similar point.
Finally, the above step is repeated to reconstruct the complete moving
path.

[0048] Finally, the keyframe association module 330 generates at least one
keyframe according to the vehicle recognition result of each of the first
monitoring frames output by the vehicle recognition system 32 and the
moving object tracking data output by the moving object tracking system
34, and establishes an association between the complete moving path of
each vehicle and the keyframes to serve as a basis for post vehicle
searching (step S430).

[0049] The keyframe association module 330 may include a keyframe database
332 and an association establishing unit 334. The keyframe database 332
stores the at least one keyframe generated according to the vehicle
recognition results of the first monitoring frames and the moving object
tracking data. The association establishing unit 334 constructs the
association between the moving path of each vehicle and the keyframes to
serve as a basis for post vehicle searching.

[0050] In detail, the keyframe association module 330 has three data input
sources, where a first data input source is the vehicle recognition
results generated by the vehicle recognition system, and the vehicle
recognition system generally generates one or multiple recognition result
images; a second data input source is the keyframes generated by the
aforementioned moving object tracking system, where one or multiple
keyframes can be generated according to different techniques; and a third
data input source is the vehicle moving path (i.e. the complete moving
path) of each of the vehicles generated by the path reconstructing module
320. Since the vehicle moving path includes data generated by moving
object tracking, one or multiple keyframes can be obtained according to
information such as time, space and monitor number, etc. of the data, and
the association between the keyframes and the vehicle path can be
established. Moreover, since the vehicle moving path also includes the
results generated by the vehicle recognition system, the recognition
result images generated by the vehicle recognition system are also
associated with the vehicle moving path.

[0051] The disclosure provides a computer-readable medium, which records a
computer program to be loaded into an electronic device to execute the
steps of the aforementioned method for reconstructing the vehicle moving
path. The computer program is formed by a plurality of program
instructions. Particularly, after the program instructions are loaded
into a computer system and executed, the steps of the aforementioned
method and a function of the system for reconstructing the vehicle moving
path are implemented. In summary, in the method, the system and the
computer-readable medium for reconstructing the vehicle moving path of
the disclosure, the vehicle recognition system and the road monitors with
relatively low cost compared to that having the vehicle recognition
function are simultaneously used, and moving object tracking data
generated according to the existing moving object tracking technique is
used to compensate inadequacy of the vehicle moving paths generated only
according to the vehicle recognition results. Moreover, according to
information such as time and monitor number, etc. in the moving object
tracking data and the vehicle recognition data, one or multiple keyframes
are obtained from the keyframe database, and the association between the
keyframes and the vehicle moving path is established to serve as basis
for post vehicle moving path inquiry.

[0052] It will be apparent to those skilled in the art that various
modifications and variations can be made to the structure of the
disclosure without departing from the scope or spirit of the disclosure.
In view of the foregoing, it is intended that the disclosure cover
modifications and variations of this disclosure provided they fall within
the scope of the following claims and their equivalents.